Background: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care. Objective: This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated. Methods: We searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. Results: We identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures. Conclusions: AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended. Trial Registration:

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.

中文翻译：

---

可应用于初级保健数据以促进癌症早期诊断的人工智能技术：系统评价

背景：2018 年，全球有超过 1700 万人被诊断患有癌症，其中英国有 36 万人。癌症预后和疾病负担高度依赖于诊断时的疾病阶段。大多数被诊断患有癌症的人首先出现在初级保健机构中，改善对癌症（通常是模糊的）症状的评估可能会导致更早发现并改善患者的治疗结果。越来越多的证据表明，人工智能 (AI) 可以帮助临床医生在某些医疗保健领域做出更好的临床决策。目的：本研究旨在系统地回顾人工智能技术，这些技术可能有助于癌症的早期诊断，并可应用于初级保健电子健康记录（EHR）数据。评估了证据的质量、人工智能技术已达到的发展阶段、证据中存在的差距以及在初级保健中使用的潜力。方法：我们检索了 2000 年 1 月 1 日至 2019 年 6 月 11 日期间的 MEDLINE、Embase、SCOPUS 和 Web of Science 数据库，纳入了所有为应用人工智能技术早期检测癌症的准确性或有效性提供证据的研究，其中可能适用于初级保健电子病历。我们纳入了所有环境和语言的所有研究设计。通过对基于人工智能的商业技术的范围审查，这些搜索得到了扩展。评估的主要结果是癌症诊断准确性的衡量。结果：我们确定了 10,456 项研究；16 项研究符合纳入标准，代表 3,862,910 名患者的数据。共有 13 项研究描述了 AI 算法的初步开发和测试，3 项研究描述了 AI 算法在独立数据集中的验证。一项研究基于前瞻性收集的数据；只有 3 项研究基于初级保健数据。我们没有发现有关实施障碍或成本效益的数据。偏倚风险评估强调了广泛的研究质量。对商业人工智能技术的额外范围审查确定了 21 项技术，其中只有 1 项符合我们的纳入标准。由于人工智能模式、数据集特征和结果测量的异质性，没有进行荟萃分析。结论：人工智能技术已应用于 EHR 类型的数据，以促进癌症的早期诊断，但其在初级保健机构中的应用仍处于成熟的早期阶段。在建议广泛采用常规初级保健临床实践之前，需要使用初级保健数据、实施障碍和成本效益来进一步证明它们的表现。试用注册：

这只是摘要。在 JMIR 网站上阅读全文。JMIR 是互联网时代电子健康和医疗保健领域领先的开放获取期刊。